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Bayesian Analysis of a Doubly Truncated ARMA-GARCH Model
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Elena Goldman
Veröffentlicht/Copyright:
6. Juni 2005
We develop a new Markov Chain Monte Carlo procedure for a time series regression model truncated by upper and lower bounds. The regression error term is assumed to follow an ARMA--GARCH process. We use a convergence diagnostics with a simultaneous test of mean and covariance stationarity and discuss model selection criteria. Using MCMC procedure we test the purchasing power parity theory for the Japanese yen controlled to fluctuate in a narrow band and find that the theory is supported if double truncation is incorporated in estimation.
Published Online: 2005-6-6
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Artikel in diesem Heft
- Article
- Economic Growth and Revealed Social Preference
- A Test of the Martingale Hypothesis
- Solving Ramsey Problems with Nonlinear Projection Methods
- A Note on the Hiemstra-Jones Test for Granger Non-causality
- Bayesian Analysis of a Doubly Truncated ARMA-GARCH Model
- What Causes The Forecasting Failure of Markov-Switching Models? A Monte Carlo Study
- Joint Tests for Non-linearity and Long Memory: The Case of Purchasing Power Parity
Artikel in diesem Heft
- Article
- Economic Growth and Revealed Social Preference
- A Test of the Martingale Hypothesis
- Solving Ramsey Problems with Nonlinear Projection Methods
- A Note on the Hiemstra-Jones Test for Granger Non-causality
- Bayesian Analysis of a Doubly Truncated ARMA-GARCH Model
- What Causes The Forecasting Failure of Markov-Switching Models? A Monte Carlo Study
- Joint Tests for Non-linearity and Long Memory: The Case of Purchasing Power Parity
